「memo」の検索結果

Etsy SEO optimization for existing and new listings. Auto-detects REWRITE (when user pastes a current title + tags + description) or CREATE (when user gives a short product context). Triggers on "rewrite this listing", "optimize my Etsy listing", "fix my title and tags", "create new listing", "make a new Etsy listing". Uses live Etsy autocomplete + competitor SERP scraping for evidence-driven keyword research. Enforces 2026 Etsy rules — tag ≤20 chars, primary keyword in first 40 chars, indexing spread across title/tags/attributes/description/hero alt, NLP-aware writing, trademark stoplist scan. When the user's problem isn't SEO (CTR, conversion, photos, price, saturated niche), it says so directly and routes to concrete next steps. Silently maintains a markdown listing database at ~/etsy-listings/ for cross-session memory on Claude/Cowork. Catches duplicate primary keywords across the user's own listings.

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Use when profiling with Intel VTune or AMD uProf for hotspots, top-down pipeline stalls, memory-bound analysis, or roofline data. Not for raw perf stat counters: use linux-perf.

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Use when writing CUDA kernels, managing the thread, block, and grid hierarchy, tiling shared memory, using streams, setting nvcc flags, or using Thrust. Not for kernel debugging: use cuda-debugging.

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Use when choosing kernel spinlocks versus mutexes, using RCU, seqlocks, completions, or memory barriers, or debugging scheduling-while-atomic. Not for userspace atomics: use concurrency-debugging.

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Use when collecting sampling profiles with perf record, reading perf report or perf annotate, measuring PMU counters with perf stat -e, or using PAPI instrumentation to interpret IPC, miss rates, MPKI, or memory bandwidth on Linux. Not for SVG rendering: use flamegraphs.

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Use when configuring DMA channels, circular buffer mode, double buffering, memory-to-peripheral transfers, or DMA IRQ completion on bare-metal MCUs. Not for cache theory: use cpu-cache-opt.

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Use when diagnosing cache misses with perf, fixing false sharing, choosing AoS or SoA layout, or adding software prefetch. Not for cache theory: use memory-hierarchy-and-caches.

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Use when tuning or profiling native code on Apple M-series Macs: unified memory, 16 KiB pages, Accelerate and Metal for matrix work, xctrace and leaks, Rosetta 2, or sysctl hardware queries.

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Use when the user and agent may hold different pictures of current state after a gap, sync request, or exposed stale claim. Not for persistence: use memory-update.

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Use when the user requests memory transfer to or from a named peer. Not for recalling a session: use history-recall. Not for store registration: use history-source-registry.

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Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.

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Use when C or C++ code needs memory-safety or undefined-behavior guarantees proved with CBMC, or ACSL contracts checked with Frama-C Eva or WP. Not for choosing the proof policy: use proof-driven.

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Use when a rendering path needs stable frame-time, CPU, GPU, and memory evidence against fixed targets. Not for one-shot profiling or visual quality review.

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RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.

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Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.

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Apply the Minimum Sufficient Language (MSL) principle through the MSL Kernel to write anything a reader must act on. Bind the reader and what they already know, partition facts from the machinery that produced them, emit each admitted fact as an action, a verification, a judgment, or a state, and stop when every claim is checkable and the reader can act. Use for status updates, work-in-progress reports to your own user, closeout and convergence comments, tickets, pull request and review write-ups, incident and postmortem notes, emails, memos, and any draft for readers outside the work. Also use when the user says MSL, Minimum Sufficient Language, write this for stakeholders, drop the jargon, make this readable, or asks for a paste-ready draft.

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Design and evaluate context compression strategies for long-running agent sessions. Use when agents exhaust memory, need to summarize conversation history, or when optimizing tokens-per-task rather than tokens-per-request.

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Initializes and configures Harness in a project — CI, memory, duo workflow, Codex. Use when setting up a new project or adding Harness configuration.

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What LaRuche is: architecture, butinage, memory, LaReine, and its full wiki.

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Turn photos into quiet 3:4 editorial posters using pure black ink masses, controlled stippling, pristine ivory paper, and either split comparison or full-canvas composition.

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Turn travel photos into restrained 3:4 charcoal memory studies that physically fade into clean paper, with split comparison or full-canvas composition.

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Turn uploaded photos into full-canvas 3:4 perforated watercolor travel-card posters. Use for watercolor memory cards, perforated ticket cards, travel archive layouts, or when this skill is invoked.

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OpenTSC v2.1: Soul/Shell architecture for interpersonal intelligence. Local, offline-first, with event graphs, judgment engine (K7), self-creation (K8), three-layer attributes, calibrated predictions, 11 VSM professions, and a pluggable zvec memory index (semantic search, identity resolution, jieba segmentation, emotion scoring incl. batched+cached LLM) that runs with zero extra deps. Transforms implicit social judgment into explicit, queryable, auditable, evolving intelligence.

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对 msMemScope 显存数据做全链路处理：根据用户的显存问题诉求（OOM、显存泄漏、显存占比拆解、Step 间对比等），指导并帮助用户以最合适的 msMemScope 采集配置完成显存数据采集（支持命令行、Python 接口、mstx 打点三种采集方式，覆盖训练、推理 vLLM-Ascend、FSDP、强化学习 verl 等场景）；对已采集的 memscope_dump_*.csv/db 数据做系统性解读与问题诊断（整体显存曲线趋势分析、显存使用大头按模块/组件拆解分析、显存峰值点各模块与流程占比分析，以及对显存泄漏、OOM 等典型问题的根因定位与原因分析）。

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